AI in Equipment Maintenance and Support

Papers from the AAAI Spring Symposium

In a recent paradigm shift, manufacturing companies who experience a reduction of profit margins in their traditional businesses try to maintain and grow their market share by offering their customers novel and aggressive service contracts. In these new offerings the old parts and labor billing model is replaced by guaranteed uptime. This in turn places the motivation to maintain equipment in working order on the servicing company. As a result there is a strong and renewed emphasis on AI technologies that can be used to monitor products and processes, detect incipient failures, identify possible faults (in various stages of development), determine preventive or corrective action, generate a cost-efficient repair plan, and monitor its execution. The service market delivered will include manufacturing (such as aircraft engines, appliances, locomotives, etc.) and non-manufacturing (such as financial systems, medical systems, etc.) based businesses.

Characteristics of typical challenges for AI in monitoring and diagnosis (M&D) service can be categorized into input, model, and output. In particular, input questions try to deal with real-time data streams resulting from on-line monitoring equipment. They are required to handle: throughput constraints; noise; nonstationary systems (due to linear drifts or chaotic behavior); erroneous data; data compression and information extraction. Process and product modeling tasks attempt to tackle issues involving non-stationary systems which require constant model update (adaptation, learning). In addition, the signature identification in time-series leading to fault detection and identification needs to be addressed. Moreover, the detection of new (unaccounted for) faults/ anomalies/state changes has to be dealt with.